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Personalized Local Differential Privacy Frequency Estimation Mechanisms Based on Partitioning the Domain of Real Attribute Values
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Author(s): Yunfei Li (Kunming University of Science and Technology, China & Yunnan University of Finance and Economics, China), Xiaodong Fu (Kunming University of Science and Technology, China), Li Liu (Kunming University of Science and Technology, China), Jiaman Ding (Kunming University of Science and Technology, China), Wei Peng (Kunming University of Science and Technology, China)and Lianyin Jia (Kunming University of Science and Technology, China)
Copyright: 2026
Volume: 20
Issue: 1
Pages: 40
Source title:
International Journal of Information Security and Privacy (IJISP)
Editor(s)-in-Chief: Yassine Maleh (Sultan Moulay Slimane University, Morocco)and Ahmed A. Abd El-Latif (Menoufia University, Egypt)
DOI: 10.4018/IJISP.401370
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Abstract
Existing multi-domain personalized local differential privacy (MDPLDP) mechanisms, which extend attribute domains by introducing fake values, often fail to provide adequate personalized privacy protection and limit utility in frequency estimation. To address these limitations, the authors propose two novel MDPLDP mechanisms that construct multiple domains by partitioning real attribute values, support cross-domain aggregation, and flexibly accommodate diverse privacy requirements and budgets. The methods further extend to multi-dimensional frequency estimation, catering to complex user privacy preferences. Theoretical analysis and experimental results demonstrate that our mechanisms achieve substantially lower estimation error and communication overhead, while delivering over 20% average utility improvement compared to state-of-the-art methods in both single- and multi-dimensional settings.
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